Hematoxylin　and　eosin　(H&E)　stained　colors　is　a　critical　step　in　the　digitized　pathological　diagnosis　of　cancer.　However,　differences　in　section　preparations,　staining　protocols　and　scanner　specifications　may　result　in　the　variations　of　stain　colors　in　pathological　images,　which　can　potentially　hamper　the　effectiveness　of　pathologist＇s　diagnosis　and　the　robustness.　To　alleviate　this　problem,　several　color　normalization　methods　have　been　proposed.　Most　previous　approaches　map　color　information　between　images　highly　dependent　on　a　reference　template.　However,　due　to　the　problem　that　pathological　images　are　usually　unpaired,　these　methods　cannot　produce　satisfactory　results.　In　this　work,　we　propose　an　unsupervised　color　normalization　method　based　on　channel　attention　and　long-range　residual,　using　a　technology　called　invertible　neural　networks　(INN)　to　transfer　the　stain　style　while　preserving　the　tissue　semantics　between　different　hospitals　or　centers,　resulting　in　a　virtual　stained　sample　in　the　sense　that　no　actual　stains　are　used.　In　our　method,　the　expert　does　not　need　to　choose　a　template　image.　More　specifically,　we　have　developed　a　new　unsupervised　stain　style　transfer　framework　based　on　INN　that　is　different　from　state-of-the-art　methods.　Meanwhile,　we　propose　a　new　generator　and　a　discriminator　to　further　improve　the　performance.　Our　approach　outperforms　state-of-the-art　methods　both　in　objective　metrics　and　subjective　evaluations,　yielding　an　improvement　of　1.0　dB　in　terms　of　PSNR.　Moreover,　the　amount　of　computation　of　the　proposed　network　has　been　reduced　by　33　%.　This　indicates　that　the　inference　speed　is　almost　one　third　faster　while　the　performance　is　better.